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Record W4410715726 · doi:10.3899/jrheum.2025-0390.o030

DIRECT AND INDIRECT COSTS ASSOCIATED WITH DAMAGE ACCRUAL: RESULTS FROM THE SYSTEMIC LUPUS INTERNATIONAL COLLABORATING CLINICS (SLICC) INCEPTION COHORT

2025· article· en· W4410715726 on OpenAlexaffvenueabout
Megan R.W. Barber, John Hanly, Murray B. Urowitz, Ian N Bruce, Yvan St. Pierre, Caroline Gordon, Sang‐Cheol Bae, Juanita Romero‐Díaz, Jorge Sánchez‐Guerrero, Sasha Bernatsky, Daniel J. Wallace, David Isenberg, Anisur Rahman, Joan T. Merrill, Paul R. Fortin, Dafna D. Gladman, Michelle Petri, Ellen M. Ginzler, Mary-Anne Dooley, Rosalind Ramsey‐Goldman, Susan Manzi, Andreas Jönsen, G S Alarcón, Ronald Van Vollenhoven, Cynthia Aranow, Meggan Mackay, Guillermo Ruiz‐Irastorza, S. Sam Lim, Murat İnanç, Kenneth Kalunian, Søren Jacobsen, Christine Peschken, Diane L Kamen, Anca Askanase, Ann E. Clarke

Bibliographic record

VenueThe Journal of Rheumatology · 2025
Typearticle
Languageen
FieldHealth Professions
TopicQuality and Safety in Healthcare
Canadian institutionsUniversity of ManitobaUniversité LavalCentre hospitalier de l'Université LavalUniversity of TorontoDalhousie UniversityMcGill University Health CentreUniversity of Calgary
Fundersnot available
KeywordsMedicineAccrualSystemic lupusCohortSystemic lupus erythematosusCohort studyPhysical therapyInternal medicineDiseaseAccounting

Abstract

fetched live from OpenAlex

O030 / #400 Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes ABSTRACT CONCURRENT SESSION 05: EMERGING INSIGHTS ON THE MANAGEMENT OF LUPUS MANIFESTATIONS AND COMORBIDITIES 23-05-2025 1:40 PM - 2:40 PM Background/Purpose We described the direct healthcare costs associated with damage accrual in patients in the Systemic Lupus International Collaborating Clinics (SLICC) Inception Cohort.[1] However, our estimates only included partial direct costs and indirect costs from lost productivity were not included. We supplemented our primary data by querying a cohort subset on all healthcare use and lost time in paid/unpaid labor and provide estimates of complete direct and indirect costs for the full cohort, stratified by damage. Methods Between 1999 and 2011, SLE patients from 31 centers in 10 countries were enrolled into the SLICC Inception Cohort within 15 months of diagnosis and data on disease damage (SLICC/ACR Damage Index [SDI]) and limited healthcare use (ie, hospitalizations, medications, and dialysis) were collected annually through to July 2022. Starting in 2015, 18 sites collected supplemental economic data annually (ie, visits to physicians, nonphysician healthcare professionals, and the emergency room, laboratory tests, radiological/other diagnostic procedures, outpatient surgeries, help obtaining medical care, and lost time in paid/unpaid labor). Direct costs were calculated by multiplying each health resource by its corresponding 2023 Canadian unit cost. Total indirect costs included: 1) absenteeism (time lost from paid labor because of illness), 2) presenteeism (degree of patient self-reported productivity impairment in paid/unpaid labor, based on a visual analog scale), and 3) opportunity costs (additional time patients would be working in paid/unpaid labor if not ill). Opportunity costs were calculated as the difference between the time patients reported working vs that worked by an age, sex, and geographic-matched general population in paid/unpaid labor. Indirect costs from paid/unpaid labor were valued using age-and-sex-specific wages from Statistics Canada. Multiple imputation was used to predict missing cost values for the patients in the full cohort who provided only utilization data for hospitalizations, medications, and dialysis for all observations. At each assessment, patients were assigned to one of 6 damage states (ie, SDI = 0, 1, 2, 3, 4, ≥ 5) and annual costs, both unimputed and including imputations, were stratified by SDI score. Means and 95% confidence intervals were computed and compared. Results 1694 patients (88.8% female, 48.9% White, mean age at diagnosis 34.6 years, mean disease duration at cohort enrollment 0.5 years), were followed for a mean of 10.5 (SD 5.3) years. Of these 1694 patients, 766 (89.7% female, 41.4% White, mean age at diagnosis 33.0 years, mean disease duration at cohort enrollment 0.4 years) completed the supplemental economic questionnaire. Their mean disease duration at the time of introduction of the supplemental questionnaire was 10.9 (range 3.9-19.5) years and this cohort subset provided this additional economic data for a mean of 3.5 (SD 1.9) years. Among the cohort subset completing the supplemental economic questionnaire, on average, indirect costs, primarily from unpaid labor, accounted for 81.1% of total costs (Table 1). For the full cohort, annual direct and indirect costs increased with increasing SDI (SDI=0: total costs $33,812 [95% CI $31,088, $36,537]; SDI ≥ 5: total costs $90,839 [95% CI $82,275, $99,403]) (Table 2). Table 1. Annual complete direct, indirect, and total costs (in 2023 Canadian dollars) for the cohort subset providing complete cost data, stratified by SDI (n = 2414 observations). Values are means. Table 2. Annual imputed complete direct, indirect, and total costs (in 2023 Canadian dollars) for the full cohort, stratified by SDI (n = 15,106 observations). Conclusions Patients with the highest vs the lowest SDIs incurred complete direct costs that were 5.9-fold higher and indirect costs 2.1-fold higher. However, patients with no or minimal damage still experienced considerably reduced productivity. Indirect costs exceeded direct, on average, by 4.5-fold, underscoring the importance of incorporating lost productivity in estimating the economic burden of SLE. References: [1.] Barber MRW. Arthritis Care Res 2020;72:1800-8.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.402
Teacher spread0.357 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
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